Role of Artificial Intelligence in High Throughput Diagnostics for Colorectal Cancer Current Updates

The existence of cancer has been stated as a century’s oldest challenge for the entire human race around theglobe recording a large amount of mortality per year and as per the WHO data nearly 10 million deaths were reported in 2021 worldwide besides others. Colorectal cancer is considered a major threat as this is cancer-related to the colon and rectum with an incidence of 41/1,00,000 recorded annually to overcome this challenge our medical system requires more advanced, accurate and efficient high throughput techniques for the prognosis and effective treatment of this disease. Artificial intelligence’s role in healthcare has been a matter of discussion among experts over the past few years, but more recently the spotlight has focused more specifically on the role that this technology can play in improving patient outcomes and improving the effectiveness of diagnosis and treatment processes. Artificial intelligence refers to a broad category of technologies, including machine learning, natural language processing and deep learning. Exploration of Molecular pathways with characteristics that helps in subtyping of Colorectal Cancer (CRC) leading to specific treatment response or prognosis, for the effective treatment, classification and early detection done using Artificial Intelligence based technologies have shown promising results so far, that it may be utilized to create prediction models in the current environment to distinguish between polyps, metastases, or normal cells in addition to early detection and effective cancer therapy. Nowadays many scientists are putting effort into designing such fabricating models by combining natural language processes and deep learning that can differentiate between non-adenomatous and adenomatous polyps to identify hyper-mutated tumours, genetic mutations and molecular pathways known as IDaRS strategy or iterative draw-and-rank sampling. The review study primarily focuses on the significance of emerging AI-based approaches for the diagnosis, detection, and prognosis of colorectal cancer in light of existing obstacles

Prediction of Cancer Microarray and DNA Methylation Data using Non-negative Matrix Factorization

Over the past few years, there has been a considerable spread of microarray technology in many biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms and approaches for the reduction of dimensionality of such microarray datasets. This study exploits the matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification accuracies are then compared for these algorithms. This technique gives an accuracy of 98%.Comment: 9th International Conference on Data Mining & Knowledge Management Process (CDKP 2020

Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer

Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. Fewer than 20% of patients only survive beyond five years from diagnosis. CRC is a highly preventable disease if diagnosed at the early stage of malignancy. Several screening methods like endoscopy (like colonoscopy; gold standard), imaging examination [computed tomographic colonography (CTC)], guaiac-based fecal occult blood (gFOBT), immunochemical test from faeces, and stool DNA test are available with different levels of sensitivity and specificity. The available screening methods are associated with certain drawbacks like invasiveness, cost, or sensitivity. In recent years, computer-aided systems-based screening, diagnosis, and treatment have been very promising in the early-stage detection and diagnosis of CRC cases. Artificial intelligence (AI) is an enormously in-demand, cost-effective technology, that uses various tools machine learning (ML), and deep learning (DL) to screen, diagnose, and stage, and has great potential to treat CRC. Moreover, different ML algorithms and neural networks [artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machines (SVMs)] have been deployed to predict precise and personalized treatment options. This review examines and summarizes different ML and DL models used for therapeutic intervention in CRC cancer along with the gap and challenges for AI

Elucidating the Interacting Domains of Chandipura

The nucleocapsid (N) protein of Chandipura virus (CHPV) plays a crucial role in viral life cycle, besides being an important structural component of the virion through proper organization of its interactions with other viral proteins. In a recent study, the authors had mapped the associations among CHPV proteins and shown that N protein interacts with four of the viral proteins: N, phosphoprotein (P), matrix protein (M), and glycoprotein (G). The present study aimed to distinguish the regions of CHPV N protein responsible for its interactions with other viral proteins. In this direction, we have generated the structure of CHPV N protein by homology modeling using SWISS-MODEL workspace and Accelrys Discovery Studio client 2.55 and mapped the domains of N protein using PiSQRD. The interactions of N protein fragments with other proteins were determined by ZDOCK rigid-body docking method and validated by yeast two-hybrid and ELISA. The study revealed a unique binding site, comprising of amino acids 1–30 at the N terminus of the nucleocapsid protein (N1) that is instrumental in its interactions with N, P, M, and G proteins. It was also observed that N2 associates with N and G proteins while N3 interacts with N, P, and M proteins

Comparative analysis of saponins, flavonoids, phenolics and antioxidant activities of field acclimatized and in vitro propagated Bacopa monnieri (L.) Pennell from different locations in India

259-268Bacopa monnieri (L.) Pennell, a well-documented nootropic plant, commonly called ‘herb of grace’ and traditionally Brahmi, is extensively used in herbal formulations for neurological disorder. However, variations in active phytopharmaceutically important compounds in the formulations often affect their therapeutic efficacy and market acceptance. In this study, we compared pharmaceutically important phytocompounds viz. saponins, flavonoids and phenolics along with antioxidant activities in field acclimatized B. monnieri plants from different geographical locations in India. Results have shown comparatively higher saponins, phenolics and flavonoids yield in BM-7 (field acclimatized from Haridwar, Uttranchal) and higher antioxidant activities in BM-4 (field acclimatized from Ghaziabad, Uttar Pradesh). The soil samples of the plants sources have also shown variations in the macronutrient compositions. In comparison, when propagated in in vitro conditions using four different culture media, all plants respond differently with comparatively higher dry weights in Murashige and Skoog medium (1962). Further, analyses of the phytocompounds in MS medium revealed variations in the phytocompounds yield and antioxidant activities. While BMT-3 (Jammu) and BMT-6 (Lucknow) reported around 9 to 11 fold increase in saponins yield compared to field acclimatized plants, BMT-2 (Delhi) showed 10 and 12 fold increase in total phenolic content and antioxidant activities, respectively. The studies may help understanding the role of environmental and in vitro propagation conditions in regulating biosynthesis of therapeutically important phytocompounds better, and thereby useful in developing a scalable process